A Concatenation Deep Learning Model for Photovoltaic Inspection Based on Aerial Infrared Images
Authors:
- 2 February 2026
- book chapter
- Published by Springer Nature
- p. 240-253
Abstract
No abstract availableKeywords
This publication has 28 references indexed in Scilit:
- Photovoltaic system fault detection techniques: a reviewNeural Computing and Applications, 2023
- A layer-2 solution for inspecting large-scale photovoltaic arrays through aerial LWIR multiview photogrammetry and deep learning: A hybrid data-centric and model-centric approachExpert Systems with Applications, 2023
- Fault identification for photovoltaic systems using a multi-output deep learning approachExpert Systems with Applications, 2022
- COVID-19 identification and analysis using CT scan images: Deep transfer learning-based approach2022
- Automatic fault classification in photovoltaic modules using Convolutional Neural NetworksRenewable Energy, 2021
- COVID-19 classification using deep feature concatenation techniqueJournal of Ambient Intelligence and Humanized Computing, 2021
- A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2Informatics in Medicine Unlocked, 2020
- The rapid development of the photovoltaic industry in China and related carbon dioxide abatement costsRegional Environmental Change, 2020
- A comparative study of fine-tuning deep learning models for plant disease identificationComputers and Electronics in Agriculture, 2019
- Biomedical image classification based on a feature concatenation and ensemble of deep CNNsJournal of Ambient Intelligence and Humanized Computing, 2019